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Everybody says "but they just predict tokens" as if that's not just "I hope you won't think too much about this" sleight of hand. Why does predicting the next

by stavros 8mo ago

Everybody says "but they just predict tokens" as if that's not just "I hope you won't think too much about this" sleight of hand. Why does predicting the next token mean that they aren't AGI? Please clarify the exact logical steps there, because I make a similar argument that human brains are merely electrical signals propagating, and not real intelligence, but I never really seem to convince people.

conception 8mo ago
More take an episode like Loops from Radiolab where a person’s memory resets back to a specific set of inputs/state and pretty responds the same way over and over again - very much like predicting the next token. Almost all human interaction is reflexive not thoughtful. Even now as you read this and process it, there’s not a lot of thought - but a whole lot of prediction and pattern matching going on.
ACCount37 8mo ago
"Predict next token" describes an interface. That tells you very little of what actually goes on inside the thing. You can "predict next token" using a human, an LLM, or a Markov chain.
sulam 8mo ago
Because there are some really fundamental things they cannot do with next token prediction. For instance, their memory is akin to someone who reads the phone book and memorizes the entire thing, but can't tell you what a phone number is for. Moreover, they can mimic semantic knowledge, because they have been trained on that knowledge, but take them out of their training distribution and they get into a "creative story-telling" mode very quickly. They can quote me all the rules of chess, but when it comes to actually making a chess move they break those rules with abandon simply because they didn't actually understand the rules. Chess is instructive in another way, too, in that you can get them to play a pretty solid opening game, maybe 10, 15 moves in, but then they start forgetting pieces, creating board positions that are impossible to reach, etc. They have memorized the forms of a board, know the names of the pieces, but they have no true understanding of what a chess game is. Coding is similar, they're fine when you give them Python or Bash shell scripts to write, they've been heavily trained on those, but ask them to deal with a system that has a non-standard stack and they will go haywire if you let their context get even medium sized. Something else they lack is any kind of learning efficiency as you or I would understand the concept. By this I mean the entire Internet is not sufficient to train today's models, the labs have to synthesize new data for models to train on to get sufficient coverage of a given area they want the model to be knowledgeable about. Continuous learning is a well-known issue as well, they simply don't do it. The labs have created memory, which is just more context engineering, but it's not the same as updating as you interact with them. I could go on. At the end of the day next token prediction is a sleight of hand. It produces amazingly powerful affects, I agree. You can turn this one magic trick into the illusion of reasoning, but what it's doing is more of a "one thing after another" style story-telling that is fine for a lot of things, but doesn't get to the heart of what intelligence means. If you want to call them intelligent because they can do this stuff, fine, but it's an alien kind of intelligence that is incredibly limited. A dog or a cat actually demonstrate more ability to learn, to contextualize, and to make meaning.
- stavros 8mo ago
  None of this is a logical certainty of "X, therefore Y", it's just opinions. You can trivially add memory to a model by continuing to train it, we just don't do it because it's expensive, not because it can't be done. Also, the phone book example is off the mark, because if I take a human who's never seen a phone and ask them to memorise the phone book, they would (or not), while not knowing what a phone number was for. Did you expect that a human would just come up on knowledge about phones entirely on their own, from nothing?
- bob1029 8mo ago
  Next token prediction is about predicting the future by minimizing the number of bits required to encode the past. It is fundamentally causal and has a discrete time domain. You can't predict token N+2 without having first predicted token N+1. The human brain has the same operational principles.
- pu_pe 8mo ago
  You didn't actually give an example of what the issue with next token prediction is. You just mentioned current constraints (ie generalization and learning are difficult, needs mountains of data to train, can't play chess very well) that are not fundamental problems. You can trivially train a transformer to play chess above the level any human can play at, and they would still be doing "next token prediction". I wouldn't be surprised if every single thing you list as a challenge is solved in a few years, either through improvement at a basic level (ie better architectures) or harnessing. We don't know how human brains produce intelligence. At a fundamental level, they might also be doing next token prediction or something similarly "dumb". Just because we know the basic mechanism of how LLMs work doesn't mean we can explain how they work and what they do, in a similar way that we might know everything we need to know about neurons and we still cannot fully grasp sentience.
  - Otterly99 8mo ago
    But chess models aren't trained the same way LLMs are trained. If I am not mistaken, they are trained directly from chess moves using pure reinforcement learning, and it's definitely not trivial as for instance AlphaZero took 64 TPUs to train.

by stavros 8mo ago

conception 8mo ago

More take an episode like Loops from Radiolab where a person’s memory resets back to a specific set of inputs/state and pretty responds the same way over and over again - very much like predicting the next token. Almost all human interaction is reflexive not thoughtful. Even now as you read this and process it, there’s not a lot of thought - but a whole lot of prediction and pattern matching going on.

ACCount37 8mo ago

"Predict next token" describes an interface. That tells you very little of what actually goes on inside the thing. You can "predict next token" using a human, an LLM, or a Markov chain.

sulam 8mo ago

Because there are some really fundamental things they cannot do with next token prediction. For instance, their memory is akin to someone who reads the phone book and memorizes the entire thing, but can't tell you what a phone number is for. Moreover, they can mimic semantic knowledge, because they have been trained on that knowledge, but take them out of their training distribution and they get into a "creative story-telling" mode very quickly. They can quote me all the rules of chess, but when it comes to actually making a chess move they break those rules with abandon simply because they didn't actually understand the rules. Chess is instructive in another way, too, in that you can get them to play a pretty solid opening game, maybe 10, 15 moves in, but then they start forgetting pieces, creating board positions that are impossible to reach, etc. They have memorized the forms of a board, know the names of the pieces, but they have no true understanding of what a chess game is. Coding is similar, they're fine when you give them Python or Bash shell scripts to write, they've been heavily trained on those, but ask them to deal with a system that has a non-standard stack and they will go haywire if you let their context get even medium sized. Something else they lack is any kind of learning efficiency as you or I would understand the concept. By this I mean the entire Internet is not sufficient to train today's models, the labs have to synthesize new data for models to train on to get sufficient coverage of a given area they want the model to be knowledgeable about. Continuous learning is a well-known issue as well, they simply don't do it. The labs have created memory, which is just more context engineering, but it's not the same as updating as you interact with them. I could go on. At the end of the day next token prediction is a sleight of hand. It produces amazingly powerful affects, I agree. You can turn this one magic trick into the illusion of reasoning, but what it's doing is more of a "one thing after another" style story-telling that is fine for a lot of things, but doesn't get to the heart of what intelligence means. If you want to call them intelligent because they can do this stuff, fine, but it's an alien kind of intelligence that is incredibly limited. A dog or a cat actually demonstrate more ability to learn, to contextualize, and to make meaning.

stavros 8mo ago
None of this is a logical certainty of "X, therefore Y", it's just opinions. You can trivially add memory to a model by continuing to train it, we just don't do it because it's expensive, not because it can't be done. Also, the phone book example is off the mark, because if I take a human who's never seen a phone and ask them to memorise the phone book, they would (or not), while not knowing what a phone number was for. Did you expect that a human would just come up on knowledge about phones entirely on their own, from nothing?
bob1029 8mo ago
Next token prediction is about predicting the future by minimizing the number of bits required to encode the past. It is fundamentally causal and has a discrete time domain. You can't predict token N+2 without having first predicted token N+1. The human brain has the same operational principles.
pu_pe 8mo ago
You didn't actually give an example of what the issue with next token prediction is. You just mentioned current constraints (ie generalization and learning are difficult, needs mountains of data to train, can't play chess very well) that are not fundamental problems. You can trivially train a transformer to play chess above the level any human can play at, and they would still be doing "next token prediction". I wouldn't be surprised if every single thing you list as a challenge is solved in a few years, either through improvement at a basic level (ie better architectures) or harnessing. We don't know how human brains produce intelligence. At a fundamental level, they might also be doing next token prediction or something similarly "dumb". Just because we know the basic mechanism of how LLMs work doesn't mean we can explain how they work and what they do, in a similar way that we might know everything we need to know about neurons and we still cannot fully grasp sentience.
- Otterly99 8mo ago
  But chess models aren't trained the same way LLMs are trained. If I am not mistaken, they are trained directly from chess moves using pure reinforcement learning, and it's definitely not trivial as for instance AlphaZero took 64 TPUs to train.